The invention is related generally to the field of rotary wellbore drilling. More specifically, the invention relates to methods for optimizing values of drilling variables, or parameters, to improve or optimize drilling performance.
Wellbore drilling, such as is used for petroleum exploration and production, includes rotating a drill bit while applying axial force to the drill bit. The rotation and the axial force are typically provided by equipment which includes a drilling xe2x80x9crigxe2x80x9d. The rig includes various devices thereon to lift, rotate and control segments of drill pipe which ultimately connect the drill bit to the equipment on the rig. The drill pipe includes an hydraulic passage generally in its center through which drilling fluid is pumped. The drilling fluid discharges through selected-size orifices in the bit (xe2x80x9cjetsxe2x80x9d) for the purposes of cooling the drill bit and lifting rock cuttings out of the wellbore as it is being drilled.
The speed and economy with which a wellbore is drilled, as well as the quality of the hole drilled, depend on a number of factors. These factors include, among others, the mechanical properties of the rocks which are drilled, the diameter and type of the drill bit used, the flow rate of the drilling fluid, and the rotation speed and axial force applied to the drill bit. It is generally the case that for any particular mechanical properties of rocks, a rate at which the drill bit penetrates the rock (xe2x80x9cROPxe2x80x9d) corresponds to the amount of axial force on and the rotary speed of the drill bit. The rate at which the drill bit wears out is generally related to the ROP. Various methods have been developed to optimize various drilling parameters to achieve various desirable results.
U.S. Pat. No. 5,704,436 issued to Smith et al, for example, describes a method for determining an optimum drilling power (rate at which rock is drilledxe2x80x94directly corresponding to ROP) for a selected drill bit type and rock formation having known or otherwise determinable compressive strength. Generally speaking, the method in the Smith et al ""436 patent includes developing a correlation between drilling power and wear rate for the selected bit type and for a particular formation compressive strength. Above a particular drilling power value (xe2x80x9cmaximum drilling powerxe2x80x9d), the wear rate of the selected type bit is purported to increase at an unacceptably high rate. The drilling power is controlled for an expected-to-be-drilled earth formation to a value below the maximum drilling power. One aspect of the method disclosed in the Smith et al ""436 patent is to make some prediction about compressive strength of rocks to be drilled, or being drilled, and select the drilling power to remain below the maximum drilling power for the particular compressive strength rock being or to be drilled.
U.S. Pat. No. 5,318,136 issued to Roswell et al discloses a method for optimizing drilling parameters to provide a lowest financial cost of drilling a selected portion of, or all of a wellbore. Generally speaking, a rate of penetration (xe2x80x9cROPxe2x80x9d) for a to-be-drilled earth formation is selected, by controlling rotation speed and axial force, to provide a value of ROP for which the financial cost of drilling the segment of wellbore is minimized.
Prior art methods for determining preferred or optimal values of drilling parameters typically focus on rock compressive strength as a principal independent variable. Other properties of earth formations are related to optimal values of drilling parameters.
Artificial Neural Networks (ANNs) are a relatively new data processing mechanism. ANNs emulate the neuron interconnection architecture of the human brain to mimic the process of human thought. By using empirical pattern recognition, ANNs have been applied in many areas to provide sophisticated data processing solutions to complex and dynamic problems (i.e. classification, diagnosis, decision making, prediction, voice recognition, military target identification, to name a few). Similar to the human brain""s problem solving process, ANNs use information gained from previous experience and apply that information to new problems and/or situations. The ANN uses a xe2x80x9ctraining experiencexe2x80x9d (data set) to build a system of neural interconnects and weighted links between an input layer (independent variable), a hidden layer of neural interconnects, and an output layer (the results, i.e. dependant variables). No existing model or known algorithmic relationship between these variables is required, but could be used to train the ANN. An initial determination for the output variables in the training exercise is compared with the actual values in a training data set. Differences are back-propagated through the ANN to adjust the weighting of the various neural interconnects, until the differences are reduced to the user""s error specification. Due largely to the flexibility of the learning algorithm, non-linear dependencies between the input and output layers, can be xe2x80x9clearnedxe2x80x9d from experience. Several references disclose various methods for using ANNs to solve various drilling, production and formation evaluation problems. These references include U.S. Pat. No. 6,044,325 issued to Chakravarthy et al, U.S. Pat. No. 6,002,985 issued to Stephenson et al, U.S. Pat. No. 6,021,377 issued to Dubinsky et al, U.S. Pat. No. 5,730,234 issued to Putot, U.S. Pat. No. 6,012,015 issued to Tubel and U.S. Pat. No. 5,812,068 issued to Wisler et al.
One aspect of the invention is a method for selecting a value of a drilling operating parameter. The method include entering a design parameter for a drill bit into a trained neural network, entering a value of a property of an earth formation to be drilled into the trained neural network and selecting the value of the drilling operating parameter based on an output of the trained neural network.
Another aspect of the invention is a method for selecting a design parameter for a drill bit. The method according to this aspect includes entering a property of an earth formation to be drilled by the bit into a trained neural network, and selecting the design parameter based on output of the trained neural network.
Another aspect of the invention is a method for optimizing an economic performance of a drill bit, including entering a value of a property of an earth formation to be drilled by the bit into a trained neural network, entering a design parameter of the drill bit into the trained neural network, and adjusting a value of a drilling operating parameter in response to output of the trained neural network so as to optimize a value of a parameter related to the economic performance.
Another aspect of the invention is a method for simulating performance of a drill bit drilling an earth formation, including entering a property of the earth formation into a trained neural network, entering a design parameter of the drill bit into the trained neural network, entering a drilling operating parameter into the trained neural network, and determining a value of a drilling performance parameter based on an output of the trained neural network.
Another aspect of the invention is a method for estimating change in economic performance of a drill bit in response to change in an input parameter, including entering a property of an earth formation to be drilled by the bit into a trained neural network, entering a design parameter of the bit into the trained neural network entering a drilling operating condition into the trained neural network, and varying at least one of the property of said earth formation, the design parameter and the drilling condition, and then determining a change in a value of a parameter related to the economic performance.
In the various aspects of the invention, representative formation parameters include electrical resistivity, acoustic velocity, natural gamma ray radiation, compressive strength and abrasiveness. Representative bit design parameters include cutting element count, cutting element type and hydraulic nozzle configuration. Representative drilling operating parameters include weight on bit, rotary speed of the bit and drilling fluid flow rate. Representative economic performance parameters include wear rate of the bit and rate of penetration of the bit.
In example embodiments, the neural network is trained by entering data from drilled wellbores, including data on one ore more of the formation parameters, and one or more of the bit design parameters. One example embodiment uses neural network training data from nearby wellbores to train the neural network to estimate values of a formation parameter at stratigraphic depths corresponding to that of the wellbore being drilled.